SoroushVahidi/scidocs huggingface dataset:
The effect of "open access" on citation impact: An analysis of ArXiv's condensed matter section
(2007)

This article statistically analyses how the citatio n impact of articles deposited in the Condensed Matter section of the preprint server ArX iv (hosted by Cornell University), and subsequently published in a scientific journal, compares to that of articles in the same journal that were not deposited in that archive. Its principal aim is to further illustrate and roughly estimate the effect of two factors, ‘early view’ and ‘quality bias’, upon differences in citation impact between these two se ts of papers, using citation data from Thomson Scientific’s Web of Science. It presents es imates for a number of journals in the field of condensed matter physics. In order to discriminate between an ‘open access’ effect and an early view effect, longitudinal citat ion data was analysed covering a time period as long as 7 years. Quality bias was measure d by calculating ArXiv citation impact differentials at the level of individual aut hors publishing in a journal, taking into account co-authorship. The analysis provided evide nce of a strong quality bias and early view effect. Correcting for these effects, there is in a sample of 6 condensed matter physics journals studied in detail, no sign of a general ‘open access advantage’ of paper s deposited in ArXiv. The study does provide evidence that ArXiv accelerates citation, due to the fact that that ArXiv makes papers arlier available rather than that it makes papers freely available.
Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control
(2017)

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.
No-Frills Human-Object Interaction Detection: Factorization, Appearance and Layout Encodings, and Training Techniques
(2018)

We show that with an appropriate factorization, and encodings of layout and appearance constructed from outputs of pretrained object detectors, a relatively simple model outperforms more sophisticated approaches on humanobject interaction detection. Our model includes factors for detection scores, human and object appearance, and coarse (box-pair configuration) and optionally fine-grained layout (human pose). We also develop training techniques that improve learning efficiency by: (i) eliminating train-inference mismatch; (ii) rejecting easy negatives during mini-batch training; and (iii) using a ratio of negatives to positives that is two orders of magnitude larger than existing approaches while constructing training mini-batches. We conduct a thorough ablation study to understand the importance of different factors and training techniques using the challenging HICO-Det dataset [4].
The nude image identification with adaptive skin chromatic distribution matching scheme
(2010)

This paper presents a new nude image identification system. We propose an adaptive chroma-distribution matching scheme based on face detection to on-line determine the image's skin chromatic distribution such that it can tolerate the color deviation coming from special lighting without increasing false alarm. The object detection based on machine learning approach is used to locate the face region in the test image. According to the color information of face, the matched skin chromatic distribution is selected to detect the skin objects in the test image. The texture feature, namely coarseness, is used to acquire accurate skin segmentation. The low-level but reliable geometrical constraints and the mug shot exclusion procedure are employed to further examine the skin regions. Experimental results that the overall detection rate is 86.3% show our method can achieve satisfactory performance for detecting nude images under special lighting conditions.
Robust analysis of feature spaces: color image segmentation
(1997)

A general technique for the recovery of signi cant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the signi cant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.
Locality-Sensitive Bloom Filter for Approximate Membership Query
(2012)

In many network applications, Bloom filters are used to support exact-matching membership query for their randomized space-efficient data structure with a small probability of false answers. In this paper, we extend the standard Bloom filter to Locality-Sensitive Bloom Filter (LSBF) to provide Approximate Membership Query (AMQ) service. We achieve this by replacing uniform and independent hash functions with locality-sensitive hash functions. Such replacement makes the storage in LSBF to be locality sensitive. Meanwhile, LSBF is space efficient and query responsive by employing the Bloom filter design. In the design of the LSBF structure, we propose a bit vector to reduce False Positives (FP). The bit vector can verify multiple attributes belonging to one member. We also use an active overflowed scheme to significantly decrease False Negatives (FN). Rigorous theoretical analysis (e.g., on FP, FN, and space overhead) shows that the design of LSBF is space compact and can provide accurate response to approximate membership queries. We have implemented LSBF in a real distributed system to perform extensive experiments using real-world traces. Experimental results show that LSBF, compared with a baseline approach and other state-of-the-art work in the literature (SmartStore and LSB-tree), takes less time to respond AMQ and consumes much less storage space.
FULLY AUTOMATIC STEREO-TO-MULTIVIEW CONVERSION IN AUTOSTEREOSCOPIC DISPLAYS
(2012)

Watching 3D without glasses will be the future of 3D home entertainment. However, current stereoscopic 3D content is not suited to drive autostereoscopic displays and a 3D format conversion is required. In this paper a system is presented which is capable of high quality stereo-tomultiview conversion in real-time. This allows playback of 3D Blu-ray or any other stereoscopic 3D video content without prior adaptation on most existing autostereoscopic displays in real-time. The algorithms described include image rectification, fast and reliable disparity estimation, adaptive post-processing and up-sampling of disparity maps, rendering of virtual views and interweaving these views into a format suitable for autostereoscopic displays.
Real-time decompression and visualization of animated volume data
(2001)

Interactive exploration of animated volume data is required by many application, but the huge amount of computational time and storage space needed for rendering does not allow the visualization of animated volumes by now. In this paper we introduce an algorithm running at interactive frame rates using 3d wavelet transforms that allows for any wavelet, motion compensation techniques and various encoding schemes of the resulting wavelet coefficients to be used. We analyze different families and orders of wavelets for compression ratio and the introduced error. We use a quantization that has been optimized for the visual impression of the reconstructed volume independent of the viewing. This enables us to achieve very high compression ratios while still being able to reconstruct the volume with as few visual artifacts as possible. A further improvement of the compression ratio has been achieved by applying a motion compensation scheme to exploit temporal coherency. Using these scheme we are capable of decompressing each volume of our animation at interactive frame rates, while visualizing these decompressed volumes on a single PC. We also present a number of improved visualization algorithms for high quality display using OpenGL hardware running at interactive frame rates on a standard PC.
Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets
(2001)

Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract specific material boundaries and convey subtle surface properties. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to interactively render with multi-dimensional transfer functions. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.
Utilizing Anaerobic Fungi for Two-stage Sugar Extraction and Biofuel Production from Lignocellulosic Biomass
(2017)

Lignocellulosic biomass is a vast and underutilized resource for the production of sugars and biofuels. However, the structural complexity of lignocellulosic biomass and the need for multiple pretreatment and enzymatic steps for sugar release renders this process economically challenging. Here, we report a novel approach for direct, single container, exogenous enzyme-free conversion of lignocellulosic biomass to sugars and biofuels using the anaerobic fungal isolate strain C1A. This approach utilizes simple physiological manipulations for timely inhibition and uncoupling of saccharolytic and fermentative capabilities of strain C1A, leading to the accumulation of sugar monomers (glucose and xylose) in the culture medium. The produced sugars, in addition to fungal hyphal lysate, are subsequently converted by Escherichia coli strain K011 to ethanol. Using this approach, we successfully recovered 17.0% (w/w) of alkali-pretreated corn stover (20.0% of its glucan and xylan content) as sugar monomers in the culture media. More importantly, 14.1% of pretreated corn stover (17.1% of glucan and xylan content) was recovered as ethanol at a final concentration of 28.16 mM after the addition of the ethanologenic strain K011. The high ethanol yield obtained is due to its accumulation as a minor fermentation end product by strain C1A during its initial growth phase, the complete conversion of sugars to ethanol by strain K011, and the possible conversion of unspecified substrates in the hyphal lysate of strain C1A to ethanol by strain K011. This study presents a novel, versatile, and exogenous enzyme-free strategy that utilizes a relatively unexplored group of organisms (anaerobic fungi) for direct biofuel production from lignocellulosic biomass.
The human phenotype ontology.
(2010)

A standardized, controlled vocabulary allows phenotypic information to be described in an unambiguous fashion in medical publications and databases. The Human Phenotype Ontology (HPO) is being developed in an effort to provide such a vocabulary. The use of an ontology to capture phenotypic information allows the use of computational algorithms that exploit semantic similarity between related phenotypic abnormalities to define phenotypic similarity metrics, which can be used to perform database searches for clinical diagnostics or as a basis for incorporating the human phenome into large-scale computational analysis of gene expression patterns and other cellular phenomena associated with human disease. The HPO is freely available at http://www.human-phenotype-ontology.org.
Detailed 3D Representations for Object Recognition and Modeling
(2013)

Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.
Communities' Detection in Social Networks: State of the art and perspectives
(2018)

Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities and clustering is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature, such as the algorithms of Newman and Girvan. However, most solutions have common problems: the stability and the community structures quality. In this paper, we give a state of the art, from 1970 until 2018 on the approaches as well as the algorithms of communities' detection in social networks. This study aims to gives a comparison between the different proposed architectures;
Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements
(2009)

Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity.
Effect of horseback riding versus a dynamic and static horse riding simulator
on sitting ability of children with cerebral palsy: a randomized controlled
trial
(2015)

[Purpose] A randomized controlled trial was conducted to investigate the immediate effects of horseback riding (HR) and a dynamic (DHS) and static (SHS) horse riding simulator (OSIM uGallop, Taiwan) on sitting ability of children with cerebral palsy. [Subjects and Methods] Thirty children with cerebral palsy were recruited and randomly assigned into three groups. Children received 30 minutes of exercise according to their assigned group. The Segmental Assessment of Trunk Control (SATCo) and Gross Motor Function Measure-66 (GMFM-66) sitting dimension were used to assess children in all groups both before and after the interventions. [Results] Sitting abilities were significantly improved after all interventions. Horseback riding showed the most improvement, followed by the dynamic and static horse riding simulator groups. Horseback riding also showed a significant improvement in the GMFM sitting dimension. [Conclusion] Horseback riding was the best intervention for promoting sitting ability of children with spastic cerebral palsy. However, a dynamic horse riding simulator can be a good surrogate for horseback riding when horseback riding is not available.
Bias Field Inconsistency Correction of Motion-Scattered Multislice MRI for Improved 3D Image Reconstruction
(2011)

A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.
Boosting The Benefits of Hybrid SDN
(2017)

The upgrade of a legacy network to a full software-defined networking (SDN) deployment is usually an incremental process, during which SDN switches and legacy switches coexist in the hybrid network. However, with inappropriate deployment of SDN switches and design of hybrid control, the advantages of SDN control could not exert, and it even results in performance degradation or inconsistency (e.g., loops, black-holes). Therefore, the hybrid SDN requires considerable coordination of the centralized control and distributed routing. In this paper, we propose a solution to handle the heterogeneity caused by distinct forwarding characteristics of SDN and legacy switches, therefore boosting the benefits of hybrid SDN. We plan SDN placement to enhance the SDN controllability over the hybrid network, and conduct traffic engineering considering both the forwarding characteristics of SDN and legacy switches. The experiments with various topologies show that the SDN placement planning and hybrid forwarding yield better network performance especially in the early 70% SDN deployment.
BAP: A Binary Analysis Platform
(2011)

BAP is a publicly available infrastructure for performing program verification and analysis tasks on binary (i.e., executable) code. In this paper, we describe BAP as well as lessons learned from previous incarnations of binary analysis platforms. BAP explicitly represents all side effects of instructions in an intermediate language (IL), making syntaxdirected analysis possible. We have used BAP to routinely generate and solve verification conditions that are hundreds of megabytes in size and encompass 100,000’s of assembly instructions.
EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones
(2011)

In order to facilitate the introduction of transit tracking and arrival time prediction in smaller transit agencies, we investigate an automatic, smartphone-based system which we call EasyTracker. To use EasyTracker, a transit agency must obtain smartphones, install an app, and place a phone in each transit vehicle. Our goal is to require no other input.
 This level of automation is possible through a set of algorithms that use GPS traces collected from instrumented transit vehicles to determine routes served, locate stops, and infer schedules. In addition, online algorithms automatically determine the route served by a given vehicle at a given time and predict its arrival time at upcoming stops.
 We evaluate our algorithms on real datasets from two existing transit services. We demonstrate our ability to accurately reconstruct routes and schedules, and compare our system's arrival time prediction performance with the current "state of the art" for smaller transit operators: the official schedule. Finally, we discuss our current prototype implementation and the steps required to take it from a research prototype to a real system.
Topic Chronicle Forest for Topic Discovery and Tracking
(2018)

To ease comprehension of given time-stamped corpora, we extend topic models to handle both the specificity and temporality of topics; this is a significant advance over previous models which fail to provide both views simultaneously. Our proposed model consists of the Topic Chronicle Forest(TCF) and Thematic Dirichlet Processes(TDP). TCF is a set of Topic Chronicle Trees, where each tree is a hierarchy of topics that becomes more specialized toward the leaves. Only one tree is defined in each time interval, a region, and is used for TDP to generate a document. The advantage of our approach lies in providing more compact topic organization, while preserving both the semantic of a given corpus and the thematic of each document. Experiments show that TCF is a useful extension for longitudinal topic discovery and tracking, and helps us to organize and digest data sets.
Expression of stressful experiences through writing: effects of a self-regulation manipulation for pessimists and optimists.
(1998)

This study assessed the effectiveness of a writing task designed to foster self-regulatory coping with stressful experiences to reduce medical clinic visits and to promote adjustment. Students entering college (N = 122) who were classified as optimists or pessimists by using a dispositional optimism measure participated in a self-regulation task (expressing thoughts and feelings about entering college and then formulating coping plans), a disclosure task (expressing thoughts and feelings only), or a control task (writing about trivial topics) for 3 weekly writing sessions. Among optimists, both the self-regulation task and the disclosure task reduced illness-related clinic visits during the following month; among pessimists, only the self-regulation task reduced clinic visits. In general, the self-regulation task beneficially affected mood state and college adjustment whereas the disclosure task increased grade point averages.